Peramalan Harga Nikel Berbasis Machine Learning menggunakan Algoritma Gated Recurrent Unit (GRU)
DOI:
https://doi.org/10.31315/jtp.v11i2.16120Kata Kunci:
Deep learning, Gated Recurrent Unit, (GRU), Peramalan Harga NikelAbstrak
Nikel merupakan komoditas tambang yang penting dalam pembuatan baterai kendaraan listrik (electric Vehicle (EV)). Meningkatnya produksi EV dalam upaya mengurangi emisi karbon menyebabkan tingginya kebutuhan Nikel sebagai bahan baku baterai kendaraan listrik diprediksi akan meningkat. Seiring meningkatnya produksi EV untuk menekan emisi karbon, kebutuhan nikel sebagai bahan baku baterai diproyeksikan terus naik. Kenaikan ini secara teori mendorong tekanan naik pada harga, terutama ketika pertumbuhan permintaan lebih cepat daripada kemampuan pasokan untuk bertambah dalam jangka pendek. Karena itu, tren permintaan yang meningkat menjadi salah satu faktor utama yang membentuk tren harga nikel ke depan. Selanjutnya, harga Nikel memiliki dampak signifikan terhadap keputusan investasi tambang, perkembangan ekonomi perusahaan Nikel, serta negara yang bergantung pada sumber daya Nikel. Namun, terdapat ketidakpastian mengenai tren harga Nikel di masa depan, sehingga solusi untuk permasalahan ini menarik untuk dikaji lebih dalam. Penelitian ini bertujuan untuk mengetahui hasil peramalan harga nikel jangka pendek dengan menerapkan algoritma Gated Recurren Unit (GRU) yang merupakan bagian dari kecerdasan buatan (Artificial Intelligent, mengetahui akurasi peramalan harga nikel menggunakan algoritma GRU, serta menyajikan data peramalan harga informatif kepada pengguna (user). Hasil penelitian menunjukkan GRU sangat efektif dalam melakukan peramalan harga Nikel berdasarkan evaluasi kerja model menggunakan Mean Absolute Percentage Error (MAPE) sebesar 1,21%.
Referensi
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16), 265–283.
Abdel Sabour, S. A. (2002). Mine size optimization using marginal analysis. Resources Policy, 28(3–4), 145–151. https://doi.org/10.1016/j.resourpol.2004.01.001
Anderson, K. (2024). The Role of Data Preprocessing in Machine Learning Accuracy for Heart Disease Prediction Hybrid Models for Heart Disease Prediction: Combining Neural Networks with Traditional.
Baker, T., Aibino, S., Belsito, E., Aubert, G., & Sahoo, A. (2019). Electric Vehicles Are a Multibillion Dollar Opportunity for Utilities.
Bhatia, V., Das, D., Tiwari, A. K., Shahbaz, M., & Hasim, H. M. (2018). Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach. Resources Policy, 55, 244–252. https://doi.org/10.1016/j.resourpol.2017.12.008
Chatterjee, S., & Dimitrakopoulos, R. (2020). Production scheduling under uncertainty of an open-pit mine using Lagrangian relaxation and branch-and-cut algorithm. International Journal of Mining, Reclamation and Environment, 34(5), 343–361. https://doi.org/10.1080/17480930.2019.1631427
Cho, K., Merrienboer, B. van, Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (No. arXiv:1406.1078). arXiv. https://doi.org/10.48550/arXiv.1406.1078
Chukwuemeka, N. K., Ulusow, A. H., & Sylvanus, O. M. (2024). Climate change, causes, economic impact and mitigation. International Journal of Scientific Research Updates, 8(1), 001–008. https://doi.org/10.53430/ijsru.2024.8.1.0043
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv Preprint arXiv:1412.3555, https://arxiv.org/abs/1412.3555.
Ferreira, R., & Pinto, F. (2022). An Overview of World Stainless Steel Scrap Trade in 2022. INSG SECRETARIAT BRIEFING PAPER, 38.
Gong, X., & Lin, B. (2018). Structural breaks and volatility forecasting in the copper futures market. Journal of Futures Markets, 38(3), 290–339. https://doi.org/10.1002/fut.21867
Gridin, I. (2022). Time series forecasting using deep learning: Combining PyTorch, RNN, TCN, and deep neural network models to provide production-ready prediction solutions (First edition). BPB Publications.
Hatayama, H., & Tahara, K. (2018). Adopting an objective approach to criticality assessment: Learning from the past. Resources Policy, 55, 96–102. https://doi.org/10.1016/j.resourpol.2017.11.002
International Energy Agency. (2024). Global Critical Minerals Outlook 2024. International Energy Agency (IEA) Data and Statistics. https://www.iea.org/data-and-statistics
Jordan, I. D., Sokół, P. A., & Park, I. M. (2021). Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems. Frontiers in Computational Neuroscience, 15, 678158. https://doi.org/10.3389/fncom.2021.678158
Khoshalan, H. A., Shakeri, J., Najmoddini, I., & Asadizadeh, M. (2021). Forecasting copper price by application of robust artificial intelligence techniques. Resources Policy, 73, 102239. https://doi.org/10.1016/j.resourpol.2021.102239
Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Muhammad Marier, S., Liu, Y., Chen, Y., & Xia, F. (2025). Deep learning for time series forecasting: A survey. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-025-02560-w
Lahadalia, B., Wijaya, C., Dartanto, T., & Subroto, A. (2024). Nickel Downstreaming in Indonesia: Reinventing Sustainable Industrial Policy and Developmental State in Building the EV Industry in ASEAN. JAS (Journal of ASEAN Studies), 12(1), 79–106. https://doi.org/10.21512/jas.v12i1.11128
Lewis, Colin. D. (1982). Industrial and business forecasting methods _ a practical guide to exponential smoothing and curve fitting. Butterworth Scientific.
Li, C. (2025). Enhancing Stock Price Prediction with Distributed Learning. Proceedings of the 2025 International Conference on Digital Economy and Intelligent Computing, 18–23. https://doi.org/10.1145/3746972.3746976
Mufida, farah ayu, Eltivia, N., & Riwajanti, N. indah. (2024). Time Series Forecasting of Nickel Sales in Nickel Mining Companies Listed on Indonesia Stock Exchange (IDX). eCo-Fin, 6(2), 133–142. https://doi.org/10.32877/ef.v6i2.1110
Ozdemir, A. C., Buluş, K., & Zor, K. (2022). Medium- to long-term nickel price forecasting using LSTM and GRU networks. Resources Policy, 78, 102906. https://doi.org/10.1016/j.resourpol.2022.102906
Ribeiro, A. H., Tiels, K., Aguirre, L. A., & Schön, T. B. (2020). Beyond exploding and vanishing gradients: Analysing RNN training using attractors and smoothness (No. arXiv:1906.08482). arXiv. https://doi.org/10.48550/arXiv.1906.08482
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/10.1016/j.asoc.2019.105524
United States Geological Survey. (2023). Mineral Commodity Summaries 2024. https://pubs.usgs.gov/periodicals/mcs2023/mcs2023-nickel.pdf
Wang, C., Zhang, X., Wang, M., Lim, M. K., & Ghadimi, P. (2019). Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques. Resources Policy, 63, 101414. https://doi.org/10.1016/j.resourpol.2019.101414
Widiatedja, I. G. N. P. (2021). Indonesia’s Export Ban on Nickel Ore: Does It Violate the World Trade Organization (WTO) Rules? Journal of World Trade, 55(Issue 4), 667–696. https://doi.org/10.54648/TRAD2021028
Zantvoort, K., Nacke, B., Görlich, D., Hornstein, S., Jacobi, C., & Funk, B. (2024). Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions. Npj Digital Medicine, 7(1), 361. https://doi.org/10.1038/s41746-024-01360-w
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